Evidence map›Paper›PMID 42824453›Full record

ArticleFrontiers in veterinary science2026

Spatial transcriptomic profiling of porcine tissue microarray detecting subclinical circovirus infection.

Wooseok Kim, Sunmin Song, Hyeonjeong Cho, Jong-Eun Park, Taehwan Oh

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Article in Frontiers in veterinary science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Wooseok Kim *Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
Sunmin Song *Department of Microbiology, College of Medicine, Dankook University, Cheonan, Republic of Korea.
Hyeonjeong ChoDepartment of Microbiology, College of Medicine, Dankook University, Cheonan, Republic of Korea.
Jong-Eun ParkGraduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
Taehwan OhDepartment of Microbiology, College of Medicine, Dankook University, Cheonan, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Porcine Circovirus type 2 (PCV2) is a key pathogen in pigs that induces host immunosuppression, leading to severe secondary infections such as porcine circovirus-associated disease (PCVAD), resulting in significant economic losses for the swine industry. More recently, it has emerged as a pathogen that needs to be ruled out in porcine xenotransplantation research due to issues related to latent infection. In this study, we established a methodology for distinguishing cell types and detecting the viral transcripts within porcine tissue microarrays using spatial transcriptomics techniques. Random primer capture-based Stereo-seq, followed by unsupervised cell clustering, distinguished four major microstructures within kidney tissue cores, each defined by a distinct marker gene: glomerulus (MAGI2), proximal tubule (CUBN), distal convoluted tubule (SLC8A1), and collecting duct (ERBB4). We further detected PCV2 transcripts in well-segmented proximal tubular cells. The spatial transcriptomics technique used in this study was able to detect both host and microbe transcriptomes with high sensitivity in tissue microarrays re-embedded from archival samples for diagnostic purposes. This suggests that spatial transcriptomics is an advanced pathology technology that integrates spatially resolved gene expression profiling with downstream bioinformatics and computational analysis, enabling the reconstruction of tissue microstructure and the diagnosis of emerging and re-emerging pathogens at the molecular level.

Indexed as

molecular diagnosisporcine circovirusspatial transcriptomicsStereo-seqtissue microarray (TMA)

Identifiers

PMID42824453
PMCPMC13628084

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.